Bencsik, A. (2021). The sixth generation of knowledge management – the headway of artificial intelligence. Journal of International Studies, 14(2), 84-101. doi:10.14254/2071-8330.2021/14-2/6
The sixth generation of knowledge management – the headway of artificial intelligence
Andrea Bencsik The sixth generation of knowledge managemen –
the headway of artificial intelligence
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decisions necessary for successful implementation (Forgang, 2015). Thanks to the accelerated pace of
development in the recent years, the supporting tools and informatics solutions used in the steps of the
knowledge management process today go well beyond the possibilities formulated in earlier phases of the
KM development (Rhem, 2017).
The dynamic development of information and computer technologies (microprocessors, satellite
technologies and the Internet) means that in recent years we have been talking about artificial intelligence
(AI) more and more as the methods applied in everyday organisational practice (Upshall, 2019; Pushpa,
2019). They are organic elements of the business model of many organisations and a key strategic
component of the global plans in various sectors. These technical solutions make it possible to follow the
continuous change and development of information and knowledge, to store huge databases and large
amounts of information, to share knowledge rapidly, thus ensuring the right of access of those involved.
As a result, it becomes possible for the day-to-day work of organisations, management decisions and the
development of strategy to be based on the up-to-date market information. Although studies to date have
not provided specific survey data on the impact of supporting new technologies upon organisational
success, all organisations are trying to keep pace with technological advances to the best of their ability. At
the same time, the relationship between technical and technological development needs and the tools and
conditions required for the management of organisational knowledge is not clear. By opening up new
areas of application, artificial intelligence has an impact on organisation performance, including the
implementation of steps in the knowledge management process (Lemaignan et al., 2016). Greater
efficiency, better productivity, innovativeness and reliability are in connection with a higher level of
acceptance of artificial intelligence, although this positive innovation is not heartily welcome in all the
areas (Shabbir & Anwer, 2015).
Based on this knowledge, the research questions were formulated, which the study endeavours to
answer on a theoretical level. How can the development of technological solutions support the steps of
the KM process? Through what phases, by which tools can strategic solutions be best supported? How
can more developed technological solutions meet the needs of new business models – through acquisition,
distribution, improvement and application of knowledge? Based on the responses to these questions, the
research aims to create a new logical model that was not in the focus of previous research. The model
provides an opportunity for managers to make the right decisions by estimating the success of
investments that ensure innovation. After the literature review and the system of relationships presenting
the framework of thinking, the two most relevant organisational areas (KM – AI) in their symbiosis are
presented. Following this, by observing the steps of the KM process, highlighting the area of knowledge
development and innovation, a possible AI-supported solution is presented that fundamentally influences
strategy. The study ends with the discussion of managerial decisions that can be made based on this new
model, conclusions, and limitations of the research.
2. LITERATURE REVIEW
2.1. Theoretical model
In order to make the logic of our further thinking comprehensible, the network of relationships will
be outlined (see Figure 1), which has already been formulated in the Introduction, and the following
chapters it will show the fully described connection between KM and AI, building upon the strategic
decisions deducible from the organisational operating model.
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Figure 1. Framework for the symbiosis of knowledge management and artificial intelligence
Source: based on Probst et al. (2006) model own construction.
As the figure above shows, strategic objectives and the conditions and decisions necessary for them
can be deduced from the business model. The study does not discuss the relationships that are in
connection with organisational structure, culture and other organisational processes, but it should be kept
in mind that, in reality, their impacts on the symbiosis of KM and AI cannot be neglected.
2.2. Strategy
The use of the knowledge necessary for the implementation of the organisational strategy that
ensures the functioning of the business model is determined by the knowledge management strategy of
the organisations. Questions to be answered in the development of a knowledge management strategy
include which characteristics of it are of strategic importance, the knowledge components of which
processes or products represent the most added value, and how the competitiveness creating power of
knowledge management strategy can be demonstrated. The answers vary depending on the organisation’s
market position, the status of industry and the available resources (Syed, 2017).
An integrated approach to knowledge management assumes that the organisation is aware of the
competences necessary for the implementation of the strategy and provides the infrastructure to acquire
them. Organisational culture, operational processes and organisational structure as well as supporting
human and information systems are parts of organisational infrastructure. Of these, we will only deal with
the importance of informatics later on.
In the next phase of the theoretical model, it is necessary to explore the relationships between KM
and AI. First, we will discuss the importance of artificial intelligence in general, and then its close
interaction with KM will be outlined.
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2.3. Artificial Intelligence
Artificial intelligence is one of the most fashionable terms in various segments of the economy, along
with actually being a research area that has existed for decades (Haenlein & Kaplan 2019).
AI evolved into a multidisciplinary science at the end of the 1980s and the beginning of the ‘90s that
involves expert systems, neural networks, robotics, natural language processes, speech recognition and
virtual reality (Ertel, 2017).
Artificial intelligence offers a number of advantages for both citizens, companies and the whole
society, provided it represents ethical and sustainable values. In order to be able to take full advantage of
the opportunities offered by artificial intelligence, both individuals and organisations face a serious
challenge (Cockburn et al., 2018). However, at current stage of ICT development and implementation of
AI in business processes these challenges should be tackled as they provide new opportunities for
economic growth (Bilan et al., 2019), particularly via HRM improvement (Balcerak & Woźniak, 2021), and
competitive advantages achievement due to the new possibilities of knowledge acquisition, knowledge
sharing, and knowledge application. (Wijaya & Suasih, 2020). Skills and competences need to be improved
that were only partially or not needed before. A common goal is to foster the ability of organisations to
innovate, while keeping in mind the development of an ethical and reliable artificial intelligence.
Artificial intelligence provides individual and groups with opportunities and power to achieve
creativity and productivity growth at all levels of the organisations and makes it possible to measure
outcomes. It helps organisations achieve their business goals in several of their activities more easily and
increase their profitability, thus providing space to improve their competitiveness, cut costs, increase
security and provide data on a continuous basis (Kazuo, 2017). These opportunities strengthen solutions
applicable in the steps of the knowledge management process.
2.4. Knowledge Management
Knowledge management, like artificial intelligence, is a multidisciplinary field. KM involves
psychology, epistemology, and cognitive sciences. Its aim is to enable people and organisations to
cooperate, and to distribute, develop, use and recycle knowledge (Bencsik, 2016). The understanding of
KM influences productivity growth, the increase of innovation and the enhancement of both individual
and organisational knowledge.
To understand the sixth generation of KM indicated in the title, a brief historical overview is
necessary. Although many have attempted to demarcate the phases of development and historical
formation in different ways, the most notable of these is Anklam’s classification, the rethought process of
which is described below (Bencsik et. al, 2018).
Generations of KM
The characteristic feature of the first era of evolution is that it focuses on the technology of
knowledge creation and formation. Everywhere during this period, the development and application of
information technology opportunities were identified with the operation of knowledge management
(Anklam, 2009). Later it turned out that to explore, integrate and transfer hard-to-find dimensions of tacit
knowledge the application of information technology alone is enough.
The main feature of the second era is the recognition and conscious management of the difference
between knowledge-based and experiential, problem-solving knowledge. The issue of human resource
management comes into focus when seeking to answer how to create the conditions to establish an
organisational atmosphere and culture that supports knowledge distribution, how to usefully operate
organisational groups – primarily knowledge communities, combining all this with the need for the
expression of human knowledge and intellectual capital to become as real as possible. The understanding
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of this issue importance leads to creation of favouring atmosphere of talents management, their
engagement and retaining (Bilan et al., 2020; Masoomzadeh et al., 2019) as well as improvement of
knowledge sharing process aiming at workplace innovation development (Arsawan et al., 2020). The
second generation already highlights the difficulties of the codification of tacit knowledge. Due to its
personal nature, tacit knowledge has parts that cannot actually be taught. The typical methodology of the
era is benchmarking, which helps to map the above mentioned gaps, with support from leadership and
knowledge conversion.
In the third era, thinking about knowledge goes beyond information technology, individuals, and
even organisations, and appears as a network. The purpose of third generation knowledge management in
general is nothing else than to systematically improve the organisation’s ability to mobilise knowledge in
order to increase performance. The free flow, growth and empowerment of knowledge can be achieved
through knowledge care and application, primarily with the help of network building and the
establishment of knowledge communities (Zamir, 2019). It is important to create an environment that
facilitates the unfolding of knowledge. This is the essential message of third generation (post-Nonaka)
knowledge management.
Further considering Anklam’s categorisation, the further phases of development, such as 4th and 5th
steps are also present in the life of organisations. The fourth generation focuses on the consideration of
knowledge as a capital factor and seeks to quantify it – thus strengthening the needs emerging in the
second phase, while phase 5 discusses the relationship between company competitiveness and innovation.
As can be seen, developed economic systems in the fourth and fifth generation of knowledge
management search for an expression of the value of human resources that is increasingly being raised in
company practice (Silva de Garcia, 2020). At the same time, it can be seen that radical, conceptual
advances have taken place in the first three generations, and, since then, the possibilities of development,
the consideration of human resources as a capital and its quantification and organisational application
have come to the fore, constantly emphasising the need for innovation (Bencsik & Filep, 2016).
The possible future of KM
The above mentioned eras have developed one after the other as a natural consequence of expert
thinking, organisational practice and technological development, but all of the eras listed are present side
by side at different levels of development in company practice. Successful initial knowledge management
actions or the operation of systems is not always conscious, but the direction of ambitions indicates right
decisions. The question is: what is the next step of evolution? How does the appearance of artificial
intelligence solutions influence the future of knowledge management in organisational life? If we think
through previous phases of the evolution, we can imagine two possibilities. In the first case, evolution –
although continuing on a higher level – actually returns to the initial steps and the cycle restarts. That is,
the omnipotence of IT will regain its dominance, and the rule of artificial intelligence will play the title role
(sometimes swapping the significance of goals and tools), sending the results and focuses of previous
evolutionary phases into the background. Just as knowledge management solutions were often identified
with a well-developed IT system in the first phase, now the attention is drawn to technological solutions
once again, and artificial intelligence as a higher level of IT opportunities is put in the focus of thinking.
And then, evolution begins again, following the same path as in previous periods of development, but at a
higher level (Ruparel, & Choubisa, 2020).
The other way is to become capable of managing possibilities offered by artificial intelligence
properly, and assign to the steps already taken the supporting solutions that elevate the organisational
practice of the knowledge management process to a higher actual level. This means that each step of a
KM process can be assigned solutions supported by artificial intelligence that help you take the necessary
intervention or solve the task arising in the actual phase faster, more efficiently and more successfully
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(Alani et al, 2019). Currently, none of the mentioned solutions are discussed in the literature, and there is a
lack of professional recommendations that summarise the AI solutions that best support the steps of the
KM process at the system level. Some of the methods that can be used for each step of the KM process
are mentioned below as examples.
3. AI SOLUTIONS THAT SUPPORT KM PROCESS STEPS
3.1. Probst model and AI
The possible supporting tools presented as an example are guided through the logic of the Probst’s
(2006) model below. The model, which already played a central role in Figure 1 is shown in Figure 2.
Figure 2. Probst’s model
Source: Probst et al, 2006.
Defining knowledge goals: e.g. a three-screen solution for “SAP Digital Boardroom” digital
decision support. Systems that support strategic decisions integrate rules and logic into the processing of
large data sets through artificial intelligence (Marks, 2016). Relevant information can be extracted from
accumulating and opaque data masses that make decision making easier at different organisational levels.
Thus, aligning strategic goals with the requirements of the knowledge management strategy becomes
easier. Certain decision making situations may as well be automated and also be able to make predictions.
In the case of knowledge identification, a teachable artificial intelligence system can be developed,
which can be used to customise access to company knowledge base. Corporate knowledge base can be
kept up-to-date, in which available internal knowledge holders can be found for actual strategic goals, or
the system can be made suitable for searching from external resource as well, e.g. applying SAP HANA.
After loading necessary information, the system, through learning from the answers given to the
questions, becomes able to develop an organisational knowledge base that is then automatically enhanced
with each newly emerging elements of information (Liu et al., 2020).
In the case of knowledge acquisition, through formal and informal channels, we can obtain useful
information that leads to the people whose knowledge we need. Help in this case is biometrics. It is
suitable for giving support, based on competences and human characteristics, e.g. the selection of
workforce or experts. The role of AI here is to process enormous statistical data sets based on given
parameters (Jaina et al, 2016).
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The knowledge development step is a factor that directly influences competitiveness. In neural
networks, learning through patterns is typical, when we want to extract adequate information from the
generally large data sets, and so modify system behaviour, e.g. using WATSON. Often, employees
themselves generate the necessary knowledge – the development of ideas, models, competences,
processes, etc. – and the acquisition of new knowledge necessary for this (Kuo, 2019).
Knowledge sharing is a critical sub-process of the KM cycle, and a condition of the organisation’s
survival. Cooperative robots (cobots for short) are designed to work beside humans as their direct co-
workers, exempting them from repetitive, stressful or dangerous operations. It is an increasingly common
solution of knowledge distribution that machine intelligence learns from another AI via the Internet. For
example, on a production line, it is easier to master the new workflow than program the machines one by
one. Cobots can connect to artificial intelligence systems that run on background systems or in the cloud,
so, instead of programming, they can easily be taught new workflows under the auspices of knowledge
distribution (Seriani et al., 2018).
The phase of knowledge preservation includes the action of recording, systematisation, storing and
refreshing. Knowledge-based systems store describable explicit forms of knowledge in a problem area, or
they are used to store expert knowledge that enables the management of a narrower issue. The engine of
knowledge-based systems is provided by inference systems. For instance, a central feature of case-based
reasoning (CBR) is to infer past solutions (real situation descriptions and interpretations) to solve present
a present problem. The system is capable of self-teaching, of learning from experiences, providing it
receives feedback. So it is not only able to store information, but in a given situation, it makes new
searches easier, simpler and shorter (Lesniak & Zima 2018).
Possessing knowledge does not provide results in itself, but its application does. The user-friendly
nature of the technology that supports access to knowledge helps employees with knowledge use. As an
example, using text-based AI can increase process efficiency, problem solving speed, or the quality of
customer service. For example, they can be used especially well in the case of financial institutions and
contact centres. In addition to an intelligent search function that can be applied to exact terms or even
complex sentences, the system is able to propose emails or chat responses, to visualise data sets, or the
intelligent escalation can help make bottleneck processes more efficient (George et al, 2017).
When measuring knowledge, the enforcement of the system approach and the review of
relationships are essential. IoT and/or content analysis can help. Features related to human resources
mostly belong to the invisible category in companies’ balance sheets, yet it was not correct to exclude
them when evaluating performance as an organisation’s value lies in organisational members, in the mind
of humans, and this invisible capital is worthless without humans, or perhaps it does not even exist (Mittal
et al, 2019).
In order to increase transparency, Table 1 below summarises the best-known tools already in
practical use, which are merely examples of support solutions that can be used in individual steps.
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Table 1
Possible AI support for KM steps
Digitisation areas
Artificial intelligence solutions Building blocks of KM
Processes Methodological background,
management tool box
Strategic knowledge goals
Setting normative, strategic and operative goals – knowledge organisation, preparation, qualification of directions of innovation, creation of access opportunities
Network building Decision models Brain storming
OCR (optical character recognition), text generation from data, deep learning, identity management, Digital Boardroom, probabilistic networks, etc.
Knowledge identification
Comparison of existing knowledge and knowledge required to implement a strategy
Benchmarking, Knowledge map, Competence map, databases, data storages, document management systems
Machine learning (self-teaching system), content-based semantic search, smart response suggestion system, keyword recognition and self-reflexion, Big Data solutions, cloud-based service
Knowledge acquisition
Acquisition of missing knowledge from external and/or internal resources
Company records, learning, competitor monitoring, market information, Peer Assist, Lessons learned, After Action Review, etc.
OCR, text analysis, biometrics, chatbot, content-based semantic search, text mining, competitor intelligence (CI), language translators, speech recognition (hidden Markov models), Attract, Onboard
Knowledge development /Knowledge creation
Combining, supplementing, updating and refreshing existing and acquired knowledge
Learning, e-learning, follow-up, brain storming, talent programs, training and career plan, development programs, language teaching, building professional communities, life long learning, blended learning
Dissemination of the necessary knowledge to all stakeholders, information distribution
Knowledge relay, on-the–job, video sharing, videoconference, wikis, World Café, Storytelling, blogs, documentation systems, discussions, forums, brainstorming, ROCK (Retention of Critical Knowledge), etc.
Virtualisation, speech recognition systems, text analysis, Google Docs, SharePoint, Social Learning, MyNet, GrapeVINE, search tools supported by artificial intelligence, company intelligent escalation system, DeepCoder, case-based reasoning, artificial neural network,, genetic algorithm, agent approach, language translators, speech recognition (hidden Markov models)
Knowledge preservation/ recording
Cases that have occurred, typical problems solutions, recording, necessary retrieval and application of all past information and knowledge
Records, work instructions, data storages, catalogues, paper-based and electronic forms, archive training and educational materials, etc.
Expert systems, DeepCoder, knowledge-based system, case-based reasoning, meaning-based search programs, personal knowledge management and content development software
Use of knowledge
Acquired, up-to-date knowledge application and integration into work processes
AAR (After Action Review), Retrospect, lessons learned, informal discussions, team work, Big Data, etc.
